The fundamental and often challenging problem of discovering cause-effect relationships has been the center of attention for the past decades, mainly among statisticians [1]}, and other disciplines such as epidemiology [2]} and computational biology [3]}. Inferring causal relationships is critical for understanding the underlying mechanisms behind random variables, and predicting the effect of externally imposed manipulations on causal dynamics of systems [4]}. For instance, in medicine, identification of the factors causing disease to find treatments and especially, to discover preventive strategies is of utmost importance [5]}.
Traditional causal discovery (also known as DAG discovery) techniques tend to rely on centralized data for inference. However, when dealing with sensitive information, e.g., medical records, preserving privacy is indisputably crucial and is often protected by strict regulations. As a result, such institutions often refrain from disclosing sensitive patients' data. The concern about privacy is certainly not limited to the field of medicine and can arise in other domains like economics, biology, or engineering, e.g., autonomous driving [6]}, [7]}. Hence, there is a need to address privacy concerns by exploring causal discovery methods built to utilize decentralize data in a privacy-preserving manner.
